Recent deep generative inpainting methods use attention layers to allow the generator to
explicitly borrow feature patches from the known region to complete a missing region. Due to …

Inpainting methods aim to restore missing parts of corrupted images and play a critical role
in many computer vision applications, such as object removal and image restoration …

Image inpainting aims to generate realistic content for missing regions of an image. Existing
methods often struggle to produce visually coherent content for missing regions of an image …

Although existing image inpainting approaches have been able to produce visually realistic
and semantically correct results, they produce only one result for each masked input. In …

Many recent inpainting works have achieved impressive results by leveraging Deep Neural
Networks (DNNs) to model various prior information for image restoration. Unfortunately, the …

Image inpainting methods have shown significant improvements by using deep neural
networks recently. However, many of these techniques often create distorted structures or …

Image inpainting is an ill-posed problem to recover missing or damaged image content
based on incomplete images with masks. Previous works usually predict the auxiliary …

In this paper, we focus on image inpainting task, aiming at recovering the missing area of an
incomplete image given the context information. Recent development in deep generative …

The latest deep learning-based approaches have shown promising results for the
challenging task of inpainting missing regions of an image. However, the existing methods …

Deep generative approaches have recently made considerable progress in image
inpainting by introducing structure priors. Due to the lack of proper interaction with image …